ESTRO 2024 - Abstract Book

S3515

Physics - Dose prediction, optimisation and applications of photon and electron planning

ESTRO 2024

Both KPB and U-Net models lead to clinically acceptable results. Differences were small between the estimated and final dose calculation and the clinical results for both KPB models and the U-Net model.

Keywords: Knowledge-Based Planning, Deep Learning, U-net

References:

1] Brunt AM, Haviland JS, Sydenham M, Agrawal RK, Algurafi H, Alhasso A, et al. Ten-year results of fast: A randomized controlled trial of 5-fraction whole-breast radiotherapy for early breast cancer. J Clin Oncol 2020;38:3261–72. https://doi.org/10.1200/JCO.19.02750. [2] Taylor C, Duane FK, Dodwell D, Gray R, Wang Z, Wang Y, et al. Estimating the Risks of Breast cancer radiotherapy: Evidence from modern radiation doses to the lungs and Heart and From previous randomized trials. J Clin Oncol 2017;35:1641–9. https://doi.org/10.1200/JCO.2016.72.0722. [3] Wang J, Hu W, Yang Z, Chen X, Wu Z, Yu X, et al. Is it possible for knowledge-based planning to improve intensity modulated radiation therapy plan quality for planners with different planning experiences in left-sided breast cancer patients? Radiat Oncol 2017;12:1–8. https://doi.org/10.1186/s13014-017-0822-z. [5] Hedden N, Xu H. Radiation therapy dose prediction for left-sided breast cancers using two-dimensional and three-dimensional deep learning models. Phys Medica 2021;83:101–7. https://doi.org/10.1016/j.ejmp.2021.02.021. [6] Fogliata A, Parabicoli S, Paganini L, Reggiori G, Lobefalo F, Cozzi L, et al. Knowledge-based DVH estimation and optimization for breast VMAT plans with and without avoidance sectors. Radiat Oncol 2022;17:1–11. https://doi.org/10.1186/s13014-022-02172-6. [7] Bakx N, Bluemink H, Hagelaar E, van der Sangen M, Theuws J, Hurkmans C. Development and evaluation of radiotherapy deep learning dose prediction models for breast cancer. Phys Imaging Radiat Oncol 2021;17:65–70. https://doi.org/10.1016/J.PHRO.2021.01.006. [8] Ahn SH, Kim ES, Kim C, Cheon W, Kim M, Lee SB, et al. Deep learning method for prediction of patient specific dose distribution in breast cancer. Radiat Oncol 2021;16:1–13. https://doi.org/10.1186/s13014-021-01864 9. [9] Tol JP, Dahele M, Delaney AR, Slotman BJ, Verbakel WFAR. Can knowledge-based DVH predictions be used for automated, individualized quality assurance of radiotherapy treatment plans? Radiat Oncol 2015;10:1–14. https://doi.org/10.1186/s13014-015-0542-1. [10] Hurkmans C, Duisters C, Peters-Verhoeven M, Boersma L, Verhoeven K, Bijker N, et al. Harmonization of breast cancer radiotherapy treatment planning in the Netherlands. Tech Innov Patient Support Radiat Oncol 2021;19:26–32. https://doi.org/10.1016/j.tipsro.2021.06.004. [11] Wang M, Zhang Q, Lam S, Cai J, Yang R. A Review on Application of Deep Learning Algorithms in External Beam Radiotherapy Automated Treatment Planning. Front Oncol 2020;10. https://doi.org/10.3389/fonc.2020.580919. [4] Kearney V, Chan JW, Haaf S, Descovich M, Solberg TD. DoseNet: A volumetric dose prediction algorithm using 3D fully-convolutional neural networks. Phys Med Biol 2018;63. https://doi.org/10.1088/1361-6560/aaef74.